---
license: apache-2.0
datasets:
- open-r1/OpenR1-Math-220k
- yentinglin/s1K-1.1-trl-format
- simplescaling/s1K-1.1
language:
- en
metrics:
- accuracy
base_model: yentinglin/Mistral-Small-24B-Instruct-2501-reasoning
pipeline_tag: text-generation
tags:
- reasoning
- mlx
- mlx-my-repo
model-index:
- name: yentinglin/Mistral-Small-24B-Instruct-2501-reasoning
  results:
  - task:
      type: text-generation
    dataset:
      name: MATH-500
      type: MATH
    metrics:
    - type: pass@1
      value: 0.95
      name: pass@1
      verified: false
    source:
      url: https://huggingface.co/spaces/yentinglin/zhtw-reasoning-eval-leaderboard
      name: yentinglin/zhtw-reasoning-eval-leaderboard
  - task:
      type: text-generation
    dataset:
      name: AIME 2025
      type: AIME
    metrics:
    - type: pass@1
      value: 0.5333
      name: pass@1
      verified: false
    - type: pass@1
      value: 0.6667
      name: pass@1
      verified: false
    source:
      url: https://huggingface.co/spaces/yentinglin/zhtw-reasoning-eval-leaderboard
      name: yentinglin/zhtw-reasoning-eval-leaderboard
  - task:
      type: text-generation
    dataset:
      name: GPQA Diamond
      type: GPQA
    metrics:
    - type: pass@1
      value: 0.62022
      name: pass@1
      verified: false
    source:
      url: https://huggingface.co/spaces/yentinglin/zhtw-reasoning-eval-leaderboard
      name: yentinglin/zhtw-reasoning-eval-leaderboard
---

# kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit

The Model [kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit](https://huggingface.co/kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit) was converted to MLX format from [yentinglin/Mistral-Small-24B-Instruct-2501-reasoning](https://huggingface.co/yentinglin/Mistral-Small-24B-Instruct-2501-reasoning) using mlx-lm version **0.22.3**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```